Differential abundance modelling of SIP gradients

Here we attempt to detect ASVs that were labelled with 13C in our soil incubations using differential abundance modelling. Using DESeq2 (Love, Huber and Anders 2014) we compare the relative abundance of each ASV in the fractions where 13C-labelled RNA is expected to be found (>1.795 g ml-1; AKA ‘heavy’ fractions) to the fractions where unlabelled RNA is expected to be found (<1.795 g ml-1; AKA ‘light’ fractions). The method has been previously described in Angel et al., (2018).

Subset the dataset

Because the DESeq2 models will be run on each gradient separately, we need to subset This is easily done using HTSSIP::phyloseq_subset (Youngblut, Barnett and Buckley 2018)

Beta diversity analysis

Let us look first at the dissimilarity in community composition between the different fractions. If the labelling was strong enough we should see a deivation of (some of) the heavy fractions from the light ones. However, a lack of a significant deviation does not mean unsuccesful labelling because if only a small minority of the community was labelled we might not see it here (but we will, hopefully, see it using DESeq2 modelling).

## 
## Call:
## adonis(formula = vegdist(otu_table(Ps_obj_SIP), method = "horn") ~      Site * Oxygen * Hours + Lib.size, data = as(sample_data(Ps_obj_SIP),      "data.frame"), permutations = 999) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                    Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)    
## Site                1    27.443 27.4431  480.48 0.53921  0.001 ***
## Oxygen              1     5.290  5.2896   92.61 0.10393  0.001 ***
## Hours               1     0.160  0.1596    2.80 0.00314  0.054 .  
## Lib.size            1     0.078  0.0777    1.36 0.00153  0.244    
## Site:Oxygen         1     0.540  0.5404    9.46 0.01062  0.001 ***
## Site:Hours          1     0.419  0.4191    7.34 0.00823  0.003 ** 
## Oxygen:Hours        1     0.119  0.1185    2.08 0.00233  0.116    
## Site:Oxygen:Hours   1     0.226  0.2259    3.96 0.00444  0.021 *  
## Residuals         291    16.621  0.0571         0.32657           
## Total             299    50.895                 1.00000           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## adonis(formula = vegdist(otu_table(Ps_obj_SIP_scaled), method = "horn") ~      Site * Oxygen * Hours + Lib.size, data = as(sample_data(Ps_obj_SIP_scaled),      "data.frame"), permutations = 999) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                    Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)    
## Site                1    27.658 27.6580  482.57 0.53919  0.001 ***
## Oxygen              1     5.381  5.3811   93.89 0.10490  0.001 ***
## Hours               1     0.161  0.1611    2.81 0.00314  0.054 .  
## Lib.size            1     0.069  0.0691    1.21 0.00135  0.304    
## Site:Oxygen         1     0.582  0.5817   10.15 0.01134  0.001 ***
## Site:Hours          1     0.426  0.4265    7.44 0.00831  0.001 ***
## Oxygen:Hours        1     0.112  0.1115    1.95 0.00217  0.139    
## Site:Oxygen:Hours   1     0.228  0.2279    3.98 0.00444  0.021 *  
## Residuals         291    16.678  0.0573         0.32514           
## Total             299    51.295                 1.00000           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Call:
## adonis(formula = vegdist(otu_table(Ps_obj_SIP_scaled), method = "horn") ~      Site * Oxygen * Hours * Density.zone, data = as(sample_data(Ps_obj_SIP_scaled),      "data.frame"), permutations = 999) 
## 
## Permutation: free
## Number of permutations: 999
## 
## Terms added sequentially (first to last)
## 
##                                 Df SumsOfSqs MeanSqs F.Model      R2 Pr(>F)    
## Site                             1    27.658 27.6580  717.77 0.53919  0.001 ***
## Oxygen                           1     5.381  5.3811  139.65 0.10490  0.001 ***
## Hours                            1     0.161  0.1611    4.18 0.00314  0.025 *  
## Density.zone                     1     2.449  2.4491   63.56 0.04774  0.001 ***
## Site:Oxygen                      1     0.513  0.5133   13.32 0.01001  0.001 ***
## Site:Hours                       1     0.405  0.4054   10.52 0.00790  0.001 ***
## Oxygen:Hours                     1     0.078  0.0778    2.02 0.00152  0.139    
## Site:Density.zone                1     0.189  0.1885    4.89 0.00367  0.006 ** 
## Oxygen:Density.zone              1     2.813  2.8134   73.01 0.05485  0.001 ***
## Hours:Density.zone               1     0.032  0.0320    0.83 0.00062  0.463    
## Site:Oxygen:Hours                1     0.174  0.1735    4.50 0.00338  0.012 *  
## Site:Oxygen:Density.zone         1     0.065  0.0654    1.70 0.00128  0.190    
## Site:Hours:Density.zone          1     0.109  0.1088    2.82 0.00212  0.057 .  
## Oxygen:Hours:Density.zone        1     0.229  0.2294    5.95 0.00447  0.002 ** 
## Site:Oxygen:Hours:Density.zone   1     0.095  0.0946    2.46 0.00184  0.098 .  
## Residuals                      284    10.943  0.0385         0.21334           
## Total                          299    51.295                 1.00000           
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
## 
## Response: Distances
##            Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)  
## Groups      1  0.2375 0.237546 5.5678    999  0.016 *
## Residuals 298 12.7139 0.042664                       
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
## 
## Response: Distances
##            Df  Sum Sq   Mean Sq      F N.Perm Pr(>F)  
## Groups      1 0.03023 0.0302278 3.1681    999  0.087 .
## Residuals 298 2.84331 0.0095413                       
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
## 
## Response: Distances
##            Df Sum Sq  Mean Sq      F N.Perm Pr(>F)   
## Groups      4 0.2595 0.064877 4.7232    999  0.002 **
## Residuals 295 4.0520 0.013736                        
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Permutation test for homogeneity of multivariate dispersions
## Permutation: free
## Number of permutations: 999
## 
## Response: Distances
##            Df Sum Sq Mean Sq     F N.Perm Pr(>F)    
## Groups      1 0.3893 0.38928 35.36    999  0.001 ***
## Residuals 298 3.2806 0.01101                        
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Differential abundance models

Now run the differential abundance models using DESeq2. We then filter the resutls to include only ASVs with Log_2_ fold change >LFC_thresh and significant at P<alpha_thresh. Lastly, we run ‘LFC-shrinking’ based on Stephens (2016).

## [1] "Certovo & 12 h & Anoxic & Labelled"
## 
## out of 3026 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up)    : 0, 0%
## LFC < -0.26 (down) : 0, 0%
## outliers [1]       : 3, 0.099%
## low counts [2]     : 7, 0.23%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
## 
## [1] "Certovo & 216 h & Anoxic & Labelled"
## 
## out of 2878 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up)    : 37, 1.3%
## LFC < -0.26 (down) : 0, 0%
## outliers [1]       : 0, 0%
## low counts [2]     : 1484, 52%
## (mean count < 2)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
## 
## [1] "Certovo & 24 h & Anoxic & Labelled"
## 
## out of 2755 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up)    : 1, 0.036%
## LFC < -0.26 (down) : 0, 0%
## outliers [1]       : 0, 0%
## low counts [2]     : 4, 0.15%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
## 
## [1] "Certovo & 48 h & Anoxic & Labelled"
## 
## out of 2787 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up)    : 15, 0.54%
## LFC < -0.26 (down) : 0, 0%
## outliers [1]       : 0, 0%
## low counts [2]     : 1222, 44%
## (mean count < 1)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
## 
## [1] "Certovo & 216 h & Anoxic & Unlabelled"
## 
## out of 2825 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up)    : 0, 0%
## LFC < -0.26 (down) : 0, 0%
## outliers [1]       : 0, 0%
## low counts [2]     : 5, 0.18%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
## 
## [1] "Certovo & 12 h & Oxic & Labelled"
## 
## out of 3053 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up)    : 7, 0.23%
## LFC < -0.26 (down) : 0, 0%
## outliers [1]       : 0, 0%
## low counts [2]     : 8, 0.26%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
## 
## [1] "Certovo & 24 h & Oxic & Labelled"
## 
## out of 2954 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up)    : 107, 3.6%
## LFC < -0.26 (down) : 0, 0%
## outliers [1]       : 0, 0%
## low counts [2]     : 905, 31%
## (mean count < 1)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
## 
## [1] "Certovo & 48 h & Oxic & Labelled"
## 
## out of 2882 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up)    : 122, 4.2%
## LFC < -0.26 (down) : 0, 0%
## outliers [1]       : 0, 0%
## low counts [2]     : 331, 11%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
## 
## [1] "Certovo & 72 h & Oxic & Labelled"
## 
## out of 2898 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up)    : 142, 4.9%
## LFC < -0.26 (down) : 0, 0%
## outliers [1]       : 1, 0.035%
## low counts [2]     : 1329, 46%
## (mean count < 1)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
## 
## [1] "Certovo & 72 h & Oxic & Unlabelled"
## 
## out of 2979 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up)    : 0, 0%
## LFC < -0.26 (down) : 0, 0%
## outliers [1]       : 6, 0.2%
## low counts [2]     : 0, 0%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
## 
## [1] "Plesne & 12 h & Anoxic & Labelled"
## 
## out of 3031 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up)    : 27, 0.89%
## LFC < -0.26 (down) : 0, 0%
## outliers [1]       : 0, 0%
## low counts [2]     : 1678, 55%
## (mean count < 3)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
## 
## [1] "Plesne & 216 h & Anoxic & Labelled"
## 
## out of 3210 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up)    : 0, 0%
## LFC < -0.26 (down) : 0, 0%
## outliers [1]       : 116, 3.6%
## low counts [2]     : 3, 0.093%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
## 
## [1] "Plesne & 24 h & Anoxic & Labelled"
## 
## out of 2848 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up)    : 22, 0.77%
## LFC < -0.26 (down) : 0, 0%
## outliers [1]       : 0, 0%
## low counts [2]     : 2340, 82%
## (mean count < 15)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
## 
## [1] "Plesne & 48 h & Anoxic & Labelled"
## 
## out of 2758 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up)    : 12, 0.44%
## LFC < -0.26 (down) : 0, 0%
## outliers [1]       : 0, 0%
## low counts [2]     : 1527, 55%
## (mean count < 2)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
## 
## [1] "Plesne & 216 h & Anoxic & Unlabelled"
## 
## out of 2862 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up)    : 0, 0%
## LFC < -0.26 (down) : 0, 0%
## outliers [1]       : 0, 0%
## low counts [2]     : 2, 0.07%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
## 
## [1] "Plesne & 12 h & Oxic & Labelled"
## 
## out of 2923 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up)    : 57, 2%
## LFC < -0.26 (down) : 0, 0%
## outliers [1]       : 1, 0.034%
## low counts [2]     : 1790, 61%
## (mean count < 1)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
## 
## [1] "Plesne & 24 h & Oxic & Labelled"
## 
## out of 2548 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up)    : 101, 4%
## LFC < -0.26 (down) : 0, 0%
## outliers [1]       : 0, 0%
## low counts [2]     : 1061, 42%
## (mean count < 1)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
## 
## [1] "Plesne & 48 h & Oxic & Labelled"
## 
## out of 2531 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up)    : 13, 0.51%
## LFC < -0.26 (down) : 0, 0%
## outliers [1]       : 0, 0%
## low counts [2]     : 1820, 72%
## (mean count < 3)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
## 
## [1] "Plesne & 72 h & Oxic & Labelled"
## 
## out of 2556 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up)    : 115, 4.5%
## LFC < -0.26 (down) : 0, 0%
## outliers [1]       : 0, 0%
## low counts [2]     : 1113, 44%
## (mean count < 1)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results
## 
## [1] "Plesne & 72 h & Oxic & Unlabelled"
## 
## out of 2798 with nonzero total read count
## adjusted p-value < 0.05
## LFC > 0.26 (up)    : 3, 0.11%
## LFC < -0.26 (down) : 0, 0%
## outliers [1]       : 7, 0.25%
## low counts [2]     : 0, 0%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results

Plot differential abundance models

# ps_obj <- Ps_obj_SIP
# DESeq_results <- DESeq_res_SIP_byTime_LFC0.322_l[9]
# plot_DESeq(DESeq_results, ps_obj, plot_title = names(DESeq_results))

DESeq_plots <- map(seq(length(DESeq_res_SIP_byTime_LFC_shrink_l)), 
                        ~plot_DESeq(DESeq_res_SIP_byTime_LFC_shrink_l[.x],  
                                                Ps_obj_SIP, plot_title = names(DESeq_res_SIP_byTime_LFC_shrink_l[.x])))

Certovo_DESeq <- ((DESeq_plots[[6]] + 
                     theme(legend.position = "none") +
                     theme(axis.text.x = element_blank())) +
                    (DESeq_plots[[1]] + 
                       theme(legend.position = "none", 
                             axis.text.x = element_blank(), 
                             axis.title.y = element_blank())) +
                    (DESeq_plots[[7]] + 
                       theme(legend.position = "none",
                             axis.text.x = element_blank())) +
                    (DESeq_plots[[3]] + 
                       theme(legend.position = "none", 
                             axis.text.x = element_blank(), 
                             axis.title.y = element_blank())) +
                    (DESeq_plots[[8]] + 
                       theme(legend.position = "none") +
                       theme(legend.position = "none",
                             axis.text.x = element_blank())) +
                    (DESeq_plots[[4]] + 
                       theme(legend.position = "none") +
                       theme(legend.position = "none", 
                             axis.text.x = element_blank(), 
                             axis.title.y = element_blank()) +
                       ylim(NA, 5)) +
                    (DESeq_plots[[9]] + 
                       theme(legend.position = "none",
                             axis.text.x = element_blank())) +
                    (DESeq_plots[[2]] + 
                       theme(legend.position = "none", 
                             axis.text.x = element_blank(), 
                             axis.title.y = element_blank())) +
                    (DESeq_plots[[10]] + 
                       theme(legend.position = "none")) +
                    (DESeq_plots[[5]] + 
                       theme(legend.position = "none", 
                             axis.title.y = element_blank())) + 
                    plot_layout(ncol = 2, guides = "collect") & 
                    theme(legend.position = 'bottom'))

save_figure(paste0(fig.path, "Certovo_DESeq2"), 
            Certovo_DESeq, 
            pwidth = 14, 
            pheight = 12,
            dpi = 600)

knitr::include_graphics(paste0(fig.path, "Certovo_DESeq2", ".png"))

Plot labelled ASVs

## [[1]]
## [1] "05_Diff_abund_figures/Labelled_ASVs_Certovo_Anoxic.svgz"
## 
## [[2]]
## [1] "05_Diff_abund_figures/Labelled_ASVs_Certovo_Oxic.svgz"
## 
## [[3]]
## [1] "05_Diff_abund_figures/Labelled_ASVs_Plesne_Anoxic.svgz"
## 
## [[4]]
## [1] "05_Diff_abund_figures/Labelled_ASVs_Plesne_Oxic.svgz"
## [[1]]
## [1] "05_Diff_abund_figures/Labelled_ASVs_Certovo_Anoxic.png"
## attr(,"class")
## [1] "knit_image_paths" "knit_asis"       
## 
## [[2]]
## [1] "05_Diff_abund_figures/Labelled_ASVs_Certovo_Oxic.png"
## attr(,"class")
## [1] "knit_image_paths" "knit_asis"       
## 
## [[3]]
## [1] "05_Diff_abund_figures/Labelled_ASVs_Plesne_Anoxic.png"
## attr(,"class")
## [1] "knit_image_paths" "knit_asis"       
## 
## [[4]]
## [1] "05_Diff_abund_figures/Labelled_ASVs_Plesne_Oxic.png"
## attr(,"class")
## [1] "knit_image_paths" "knit_asis"

Plot phylogenetic trees with heatmaps

Labelled n
Labelled 778
Unlabelled 37162
NA 21148
## [[1]]
## [1] "05_Diff_abund_figures/Tree_HM_Actinobacteria.svgz"
## 
## [[2]]
## [1] "05_Diff_abund_figures/Tree_HM_Alphaproteobacteria.svgz"
## 
## [[3]]
## [1] "05_Diff_abund_figures/Tree_HM_Gammaproteobacteria.svgz"
## 
## [[4]]
## [1] "05_Diff_abund_figures/Tree_HM_Acidobacteriota.svgz"
## 
## [[5]]
## [1] "05_Diff_abund_figures/Tree_HM_Verrucomicrobiota.svgz"
## 
## [[6]]
## [1] "05_Diff_abund_figures/Tree_HM_Bacteroidota.svgz"
## 
## [[7]]
## [1] "05_Diff_abund_figures/Tree_HM_Firmicutes.svgz"

Current session info


─ Session info ─────────────────────────────────────────────────────────────────────────
 setting  value                       
 version  R version 4.0.3 (2020-10-10)
 os       Ubuntu 18.04.5 LTS          
 system   x86_64, linux-gnu           
 ui       X11                         
 language (EN)                        
 collate  en_US.UTF-8                 
 ctype    en_US.UTF-8                 
 tz       Europe/Prague               
 date     2021-02-10                  

─ Packages ─────────────────────────────────────────────────────────────────────────────
 package              * version    date       lib
 ade4                   1.7-16     2020-10-28 [1]
 annotate               1.66.0     2020-04-27 [1]
 AnnotationDbi          1.50.3     2020-07-25 [1]
 ape                    5.4-1      2020-08-13 [1]
 aplot                  0.0.6      2020-09-03 [1]
 ashr                   2.2-47     2020-02-20 [1]
 assertthat             0.2.1      2019-03-21 [1]
 backports              1.2.1      2020-12-09 [1]
 Biobase              * 2.48.0     2020-04-27 [1]
 BiocGenerics         * 0.34.0     2020-04-27 [1]
 BiocManager            1.30.10    2019-11-16 [1]
 BiocParallel           1.22.0     2020-04-27 [1]
 biomformat             1.16.0     2020-04-27 [1]
 Biostrings           * 2.56.0     2020-04-27 [1]
 bit                    4.0.4      2020-08-04 [1]
 bit64                  4.0.5      2020-08-30 [1]
 bitops                 1.0-6      2013-08-17 [1]
 blob                   1.2.1      2020-01-20 [1]
 broom                  0.7.4      2021-01-29 [1]
 cachem                 1.0.3      2021-02-04 [1]
 cellranger             1.1.0      2016-07-27 [1]
 cli                    2.3.0      2021-01-31 [1]
 clipr                  0.7.1      2020-10-08 [1]
 cluster                2.1.0      2019-06-19 [1]
 codetools              0.2-18     2020-11-04 [1]
 colorspace             2.0-0      2020-11-11 [1]
 crayon                 1.4.1      2021-02-08 [1]
 data.table             1.13.6     2020-12-30 [1]
 DBI                    1.1.1      2021-01-15 [1]
 dbplyr                 2.1.0      2021-02-03 [1]
 DelayedArray         * 0.14.1     2020-07-14 [1]
 desc                   1.2.0      2018-05-01 [1]
 DESeq2               * 1.28.1     2020-05-12 [1]
 details                0.2.1      2020-01-12 [1]
 digest                 0.6.27     2020-10-24 [1]
 dplyr                * 1.0.4      2021-02-02 [1]
 ellipsis               0.3.1      2020-05-15 [1]
 evaluate               0.14       2019-05-28 [1]
 extrafont            * 0.17       2014-12-08 [1]
 extrafontdb            1.0        2012-06-11 [1]
 farver                 2.0.3      2020-01-16 [1]
 fastmap                1.1.0      2021-01-25 [1]
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[1] /home/angel/R/library
[2] /usr/local/lib/R/site-library
[3] /usr/lib/R/site-library
[4] /usr/lib/R/library


References

Angel R, Panhölzl C, Gabriel R et al. Application of stable-isotope labelling techniques for the detection of active diazotrophs. Environ Microbiol 2018;20:44–61.

Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014;15:550.

Stephens M. False discovery rates: a new deal. Biostatistics 2016;18:275–94.

Youngblut ND, Barnett SE, Buckley DH. HTSSIP: An R package for analysis of high throughput sequencing data from nucleic acid stable isotope probing (SIP) experiments. PLOS ONE 2018;13:e0189616.